636 research outputs found

    Finite-Block-Length Analysis in Classical and Quantum Information Theory

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    Coding technology is used in several information processing tasks. In particular, when noise during transmission disturbs communications, coding technology is employed to protect the information. However, there are two types of coding technology: coding in classical information theory and coding in quantum information theory. Although the physical media used to transmit information ultimately obey quantum mechanics, we need to choose the type of coding depending on the kind of information device, classical or quantum, that is being used. In both branches of information theory, there are many elegant theoretical results under the ideal assumption that an infinitely large system is available. In a realistic situation, we need to account for finite size effects. The present paper reviews finite size effects in classical and quantum information theory with respect to various topics, including applied aspects

    Joint Source-Channel Coding Optimized On End-to-End Distortion for Multimedia Source

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    In order to achieve high efficiency, multimedia source coding usually relies on the use of predictive coding. While more efficient, source coding based on predictive coding has been considered to be more sensitive to errors during communication. With the current volume and importance of multimedia communication, minimizing the overall distortion during communication over an error-prone channel is critical. In addition, for real-time scenarios, it is necessary to consider additional constraints such as fix and small delay for a given bit rate. To comply with these requirements, we seek an efficient joint source-channel coding scheme. In this work, end-to-end distortion is studied for a first order autoregressive synthetic source that represents a general multimedia traffic. This study reveals that predictive coders achieve the same channel-induced distortion performance as memoryless codecs when applying optimal error concealment. We propose a joint source-channel system based on incremental redundancy that satisfies the fixed delay and error-prone channel constraints and combines DPCM as a source encoder and a rate-compatible punctured convolutional (RCPC) error control codec. To calculate the joint source-channel coding rate allocation that minimizes end-to-end distortion, we develop a Markov Decision Process (MDP) approach for delay constrained feedback Hybrid ARQ, and we use a Dynamic Programming (DP) technique. Our simulation results support the improvement in end-to-end distortion compared to a conventional Forward Error Control (FEC) approach with no feedback

    Joint source channel coding for progressive image transmission

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    Recent wavelet-based image compression algorithms achieve best ever performances with fully embedded bit streams. However, those embedded bit streams are very sensitive to channel noise and protections from channel coding are necessary. Typical error correcting capability of channel codes varies according to different channel conditions. Thus, separate design leads to performance degradation relative to what could be achieved through joint design. In joint source-channel coding schemes, the choice of source coding parameters may vary over time and channel conditions. In this research, we proposed a general approach for the evaluation of such joint source-channel coding scheme. Instead of using the average peak signal to noise ratio (PSNR) or distortion as the performance metric, we represent the system performance by its average error-free source coding rate, which is further shown to be an equivalent metric in the optimization problems. The transmissions of embedded image bit streams over memory channels and binary symmetric channels (BSCs) are investigated in this dissertation. Mathematical models were obtained in closed-form by error sequence analysis (ESA). Not surprisingly, models for BSCs are just special cases for those of memory channels. It is also discovered that existing techniques for performance evaluation on memory channels are special cases of this new approach. We further extend the idea to the unequal error protection (UEP) of embedded images sources in BSCs. The optimization problems are completely defined and solved. Compared to the equal error protection (EEP) schemes, about 0.3 dB performance gain is achieved by UEP for typical BSCs. For some memory channel conditions, the performance improvements can be up to 3 dB. Transmission of embedded image bit streams in channels with feedback are also investigated based on the model for memory channels. Compared to the best possible performance achieved on feed forward transmission, feedback leads to about 1.7 dB performance improvement

    Distributed space-time coding for two-way wireless relay networks

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    In this paper, we consider distributed space-time coding for two-way wireless relay networks, where communication between two terminals is assisted by relay nodes. Relaying protocols using two, three, and four time slots are proposed. The protocols using four time slots are the traditional amplify-and-forward (AF) and decode-and-forward (DF) protocols, which do not consider the property of the two-way traffic. A new class of relaying protocols, termed as partial decode-and-forward (PDF), is developed for the two time slots transmission, where each relay first removes part of the noise before sending the signal to the two terminals. Protocols using three time slots are proposed to compensate the fact that the two time slots protocols cannot make use of direct transmission between the two terminals. For all protocols, after processing their received signals, the relays encode the resulting signals using a distributed linear dispersion (LD) code. The proposed AF protocols are shown to achieve the diversity order of min{N,K}(1- (log log P/log P)), where N is the number of relays, P is the total power of the network, and K is the number of symbols transmitted during each time slot. When random unitary matrix is used for LD code, the proposed PDF protocols resemble random linear network coding, where the former operates on the unitary group and the latter works on the finite field. Moreover, PDF achieves the diversity order of min{N,K} but the conventional DF can only achieve the diversity order of 1. Finally, we find that two time slots protocols also have advantages over four-time-slot protocols in media access control (MAC) layer

    Precoding for coded communication on block fading channels and cooperative communications

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    We study precoding for the outage probability minimization of block fading (BF) channels and BF relay channels. Recently, an upper bound on the outage probability with precoding was established for BF channels, but only for high instantaneous SNR. This upper bound is much easier to minimize than the actual outage probability, so that optimal precoding matrices can be determined without much computational effort. Here, we provide a proof for the upper bound on the outage probability at low instantaneous SNR. Next, the structure of the precoding matrix is simplified so that it can be easily constructed for an arbitrary number of blocks in the BF channel. Finally, we apply this technique to cooperative communications

    Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

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    New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure
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